
Conclusion
In this project, two major models were used in Nasdaq index stock price forecasting. We
proposed a neural network model to predict the daily price of Nasdaq-100 on a short-term
scale, represented the daily stock price of the most representative Nasdaq-listed stocks, and an
improved ARIMA model was proposed to study the stock price forecasting from the Nasdaq
Composite in long-term scale, represented the monthly performance of all stocks listed on the
Nasdaq stock exchange. The attainability of the integrated models on stock price forecasting is
shown by the result, which showed a comparatively satisfying accuracy. !
Moreover, the integrated forecasting model can provide results that reflect the pattern from
different perspectives, which helps investors better understand the market trend and guide their
decisions. The experimental analysis indicated that although the neural network models are
broadly used in financial forecasting, the utilization of feature processing with neural networks in
statistical models also showed noticeable potential. It is worth noting that due to the market's
volatility, more economic indicators could be added as the training features for the model to
improve the forecasting ability further. !